{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56172696",
   "metadata": {},
   "outputs": [],
   "source": [
    "#导包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams['font.family']=['SimHei']#显示中文标签\n",
    "plt.rcParams['axes.unicode_minus']=False#用来显示正常正负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "69a47b09",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>educational level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>53.900002</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>67.199997</td>\n",
       "      <td>47.799999</td>\n",
       "      <td>70.800003</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>94.921280</td>\n",
       "      <td>95.698372</td>\n",
       "      <td>94.155960</td>\n",
       "      <td>93.074661</td>\n",
       "      <td>96.308372</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.916756</td>\n",
       "      <td>93.217621</td>\n",
       "      <td>92.629356</td>\n",
       "      <td>89.984192</td>\n",
       "      <td>94.615364</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Andorra</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>59.849655</td>\n",
       "      <td>56.631344</td>\n",
       "      <td>63.278904</td>\n",
       "      <td>27.187010</td>\n",
       "      <td>73.655067</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Anguilla</td>\n",
       "      <td>Not Classified</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Antigua and Barbuda</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.237091</td>\n",
       "      <td>94.092606</td>\n",
       "      <td>90.622528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Armenia</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>99.266441</td>\n",
       "      <td>99.112640</td>\n",
       "      <td>99.389252</td>\n",
       "      <td>99.545982</td>\n",
       "      <td>99.073601</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Australia</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Countries and areas Development Regions      Total     Female       Male  \\\n",
       "0          Afghanistan     Least Developed  53.900002  40.200001  67.199997   \n",
       "1              Albania      More Developed  94.921280  95.698372  94.155960   \n",
       "2              Algeria      Less Developed  92.916756  93.217621  92.629356   \n",
       "3              Andorra      More Developed        NaN        NaN        NaN   \n",
       "4               Angola     Least Developed  59.849655  56.631344  63.278904   \n",
       "5             Anguilla      Not Classified        NaN        NaN        NaN   \n",
       "6  Antigua and Barbuda      Less Developed        NaN        NaN        NaN   \n",
       "7            Argentina      Less Developed  92.237091  94.092606  90.622528   \n",
       "8              Armenia      Less Developed  99.266441  99.112640  99.389252   \n",
       "9            Australia      More Developed        NaN        NaN        NaN   \n",
       "\n",
       "       Rural      Urban educational level  \n",
       "0  47.799999  70.800003           Primary  \n",
       "1  93.074661  96.308372           Primary  \n",
       "2  89.984192  94.615364           Primary  \n",
       "3        NaN        NaN               NaN  \n",
       "4  27.187010  73.655067           Primary  \n",
       "5        NaN        NaN               NaN  \n",
       "6        NaN        NaN               NaN  \n",
       "7        NaN        NaN           Primary  \n",
       "8  99.545982  99.073601           Primary  \n",
       "9        NaN        NaN               NaN  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_excel(r\"EducationData.xlsx\",\"Completion-rate\")\n",
    "df.head(10)#读取第一个表的数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7fb79e91",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 606 entries, 0 to 605\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Countries and areas  606 non-null    object \n",
      " 1   Development Regions  606 non-null    object \n",
      " 2   Total                321 non-null    float64\n",
      " 3   Female               321 non-null    float64\n",
      " 4   Male                 321 non-null    float64\n",
      " 5   Rural                318 non-null    float64\n",
      " 6   Urban                318 non-null    float64\n",
      " 7   educational level    321 non-null    object \n",
      "dtypes: float64(5), object(3)\n",
      "memory usage: 38.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#查看数据情况（info）\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "603c36f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Countries and areas      0\n",
       "Development Regions      0\n",
       "Total                  285\n",
       "Female                 285\n",
       "Male                   285\n",
       "Rural                  288\n",
       "Urban                  288\n",
       "educational level      285\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()#查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8bb977a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import missingno#查看合并表data1的缺失值情况\n",
    "missingno.matrix(df,figsize=(30,5)) #以矩阵的形式"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3482f030",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>educational level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>53.900002</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>67.199997</td>\n",
       "      <td>47.799999</td>\n",
       "      <td>70.800003</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>94.921280</td>\n",
       "      <td>95.698372</td>\n",
       "      <td>94.155960</td>\n",
       "      <td>93.074661</td>\n",
       "      <td>96.308372</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.916756</td>\n",
       "      <td>93.217621</td>\n",
       "      <td>92.629356</td>\n",
       "      <td>89.984192</td>\n",
       "      <td>94.615364</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>59.849655</td>\n",
       "      <td>56.631344</td>\n",
       "      <td>63.278904</td>\n",
       "      <td>27.187010</td>\n",
       "      <td>73.655067</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.237091</td>\n",
       "      <td>94.092606</td>\n",
       "      <td>90.622528</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>37.685638</td>\n",
       "      <td>29.298988</td>\n",
       "      <td>47.933109</td>\n",
       "      <td>19.580156</td>\n",
       "      <td>39.635963</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>36.799999</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>46.799999</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.661892</td>\n",
       "      <td>26.924601</td>\n",
       "      <td>33.276043</td>\n",
       "      <td>12.900005</td>\n",
       "      <td>47.367939</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Countries and areas Development Regions      Total     Female       Male  \\\n",
       "0           Afghanistan     Least Developed  53.900002  40.200001  67.199997   \n",
       "1               Albania      More Developed  94.921280  95.698372  94.155960   \n",
       "2               Algeria      Less Developed  92.916756  93.217621  92.629356   \n",
       "4                Angola     Least Developed  59.849655  56.631344  63.278904   \n",
       "7             Argentina      Less Developed  92.237091  94.092606  90.622528   \n",
       "..                  ...                 ...        ...        ...        ...   \n",
       "598             Uruguay      Less Developed  37.685638  29.298988  47.933109   \n",
       "602            Viet Nam      Less Developed  55.500000  61.099998  50.299999   \n",
       "603               Yemen     Least Developed  29.500000  23.400000  36.799999   \n",
       "604              Zambia     Least Developed  29.661892  26.924601  33.276043   \n",
       "605            Zimbabwe      Less Developed  15.300000  13.600000  17.200001   \n",
       "\n",
       "         Rural      Urban educational level  \n",
       "0    47.799999  70.800003           Primary  \n",
       "1    93.074661  96.308372           Primary  \n",
       "2    89.984192  94.615364           Primary  \n",
       "4    27.187010  73.655067           Primary  \n",
       "7          NaN        NaN           Primary  \n",
       "..         ...        ...               ...  \n",
       "598  19.580156  39.635963   Upper secondary  \n",
       "602  48.000000  71.099998   Upper secondary  \n",
       "603  20.100000  46.799999   Upper secondary  \n",
       "604  12.900005  47.367939   Upper secondary  \n",
       "605   5.800000  29.100000   Upper secondary  \n",
       "\n",
       "[321 rows x 8 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=df.dropna(thresh=3)#删除一行中不存在3个及3个以上的存在值的行\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2d19c8b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Countries and areas    0\n",
       "Development Regions    0\n",
       "Total                  0\n",
       "Female                 0\n",
       "Male                   0\n",
       "Rural                  3\n",
       "Urban                  3\n",
       "educational level      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()#查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "03f25206",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>educational level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>53.900002</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>67.199997</td>\n",
       "      <td>47.799999</td>\n",
       "      <td>70.800003</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>94.921280</td>\n",
       "      <td>95.698372</td>\n",
       "      <td>94.155960</td>\n",
       "      <td>93.074661</td>\n",
       "      <td>96.308372</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.916756</td>\n",
       "      <td>93.217621</td>\n",
       "      <td>92.629356</td>\n",
       "      <td>89.984192</td>\n",
       "      <td>94.615364</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>59.849655</td>\n",
       "      <td>56.631344</td>\n",
       "      <td>63.278904</td>\n",
       "      <td>27.187010</td>\n",
       "      <td>73.655067</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.237091</td>\n",
       "      <td>94.092606</td>\n",
       "      <td>90.622528</td>\n",
       "      <td>53.472349</td>\n",
       "      <td>70.171760</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>37.685638</td>\n",
       "      <td>29.298988</td>\n",
       "      <td>47.933109</td>\n",
       "      <td>19.580156</td>\n",
       "      <td>39.635963</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>36.799999</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>46.799999</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.661892</td>\n",
       "      <td>26.924601</td>\n",
       "      <td>33.276043</td>\n",
       "      <td>12.900005</td>\n",
       "      <td>47.367939</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Countries and areas Development Regions      Total     Female       Male  \\\n",
       "0           Afghanistan     Least Developed  53.900002  40.200001  67.199997   \n",
       "1               Albania      More Developed  94.921280  95.698372  94.155960   \n",
       "2               Algeria      Less Developed  92.916756  93.217621  92.629356   \n",
       "4                Angola     Least Developed  59.849655  56.631344  63.278904   \n",
       "7             Argentina      Less Developed  92.237091  94.092606  90.622528   \n",
       "..                  ...                 ...        ...        ...        ...   \n",
       "598             Uruguay      Less Developed  37.685638  29.298988  47.933109   \n",
       "602            Viet Nam      Less Developed  55.500000  61.099998  50.299999   \n",
       "603               Yemen     Least Developed  29.500000  23.400000  36.799999   \n",
       "604              Zambia     Least Developed  29.661892  26.924601  33.276043   \n",
       "605            Zimbabwe      Less Developed  15.300000  13.600000  17.200001   \n",
       "\n",
       "         Rural      Urban educational level  \n",
       "0    47.799999  70.800003           Primary  \n",
       "1    93.074661  96.308372           Primary  \n",
       "2    89.984192  94.615364           Primary  \n",
       "4    27.187010  73.655067           Primary  \n",
       "7    53.472349  70.171760           Primary  \n",
       "..         ...        ...               ...  \n",
       "598  19.580156  39.635963   Upper secondary  \n",
       "602  48.000000  71.099998   Upper secondary  \n",
       "603  20.100000  46.799999   Upper secondary  \n",
       "604  12.900005  47.367939   Upper secondary  \n",
       "605   5.800000  29.100000   Upper secondary  \n",
       "\n",
       "[321 rows x 8 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 首先，选择所有数值型的列进行均值填充\n",
    "num = df.select_dtypes(include=['float', 'int']).columns\n",
    "# 计算这些列的均值\n",
    "mean_values = df[num].mean()\n",
    "# 使用均值填充相应的列\n",
    "df = df.fillna(mean_values)\n",
    "# 打印填充后的DataFrame查看结果\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be4ee3f4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 321 entries, 0 to 605\n",
      "Data columns (total 8 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Countries and areas  321 non-null    object \n",
      " 1   Development Regions  321 non-null    object \n",
      " 2   Total                321 non-null    float64\n",
      " 3   Female               321 non-null    float64\n",
      " 4   Male                 321 non-null    float64\n",
      " 5   Rural                321 non-null    float64\n",
      " 6   Urban                321 non-null    float64\n",
      " 7   educational level    321 non-null    object \n",
      "dtypes: float64(5), object(3)\n",
      "memory usage: 22.6+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()#查看缺失值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "19ec23bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 删除完全相同的重复行\n",
    "df = df.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "30fa1b4c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>53.900002</td>\n",
       "      <td>40.200001</td>\n",
       "      <td>67.199997</td>\n",
       "      <td>47.799999</td>\n",
       "      <td>70.800003</td>\n",
       "      <td>Primary</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Albania</td>\n",
       "      <td>More Developed</td>\n",
       "      <td>94.921280</td>\n",
       "      <td>95.698372</td>\n",
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       "      <td>96.308372</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.916756</td>\n",
       "      <td>93.217621</td>\n",
       "      <td>92.629356</td>\n",
       "      <td>89.984192</td>\n",
       "      <td>94.615364</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Angola</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>59.849655</td>\n",
       "      <td>56.631344</td>\n",
       "      <td>63.278904</td>\n",
       "      <td>27.187010</td>\n",
       "      <td>73.655067</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Argentina</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>92.237091</td>\n",
       "      <td>94.092606</td>\n",
       "      <td>90.622528</td>\n",
       "      <td>53.472349</td>\n",
       "      <td>70.171760</td>\n",
       "      <td>Primary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>37.685638</td>\n",
       "      <td>29.298988</td>\n",
       "      <td>47.933109</td>\n",
       "      <td>19.580156</td>\n",
       "      <td>39.635963</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>Viet Nam</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>55.500000</td>\n",
       "      <td>61.099998</td>\n",
       "      <td>50.299999</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>71.099998</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>Yemen</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.500000</td>\n",
       "      <td>23.400000</td>\n",
       "      <td>36.799999</td>\n",
       "      <td>20.100000</td>\n",
       "      <td>46.799999</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>Zambia</td>\n",
       "      <td>Least Developed</td>\n",
       "      <td>29.661892</td>\n",
       "      <td>26.924601</td>\n",
       "      <td>33.276043</td>\n",
       "      <td>12.900005</td>\n",
       "      <td>47.367939</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>Less Developed</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>13.600000</td>\n",
       "      <td>17.200001</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>29.100000</td>\n",
       "      <td>Upper secondary</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>321 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Countries and areas Development Regions      Total     Female       Male  \\\n",
       "0           Afghanistan     Least Developed  53.900002  40.200001  67.199997   \n",
       "1               Albania      More Developed  94.921280  95.698372  94.155960   \n",
       "2               Algeria      Less Developed  92.916756  93.217621  92.629356   \n",
       "4                Angola     Least Developed  59.849655  56.631344  63.278904   \n",
       "7             Argentina      Less Developed  92.237091  94.092606  90.622528   \n",
       "..                  ...                 ...        ...        ...        ...   \n",
       "598             Uruguay      Less Developed  37.685638  29.298988  47.933109   \n",
       "602            Viet Nam      Less Developed  55.500000  61.099998  50.299999   \n",
       "603               Yemen     Least Developed  29.500000  23.400000  36.799999   \n",
       "604              Zambia     Least Developed  29.661892  26.924601  33.276043   \n",
       "605            Zimbabwe      Less Developed  15.300000  13.600000  17.200001   \n",
       "\n",
       "         Rural      Urban educational level  \n",
       "0    47.799999  70.800003           Primary  \n",
       "1    93.074661  96.308372           Primary  \n",
       "2    89.984192  94.615364           Primary  \n",
       "4    27.187010  73.655067           Primary  \n",
       "7    53.472349  70.171760           Primary  \n",
       "..         ...        ...               ...  \n",
       "598  19.580156  39.635963   Upper secondary  \n",
       "602  48.000000  71.099998   Upper secondary  \n",
       "603  20.100000  46.799999   Upper secondary  \n",
       "604  12.900005  47.367939   Upper secondary  \n",
       "605   5.800000  29.100000   Upper secondary  \n",
       "\n",
       "[321 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "f9a21f41",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>321.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>321.000000</td>\n",
       "      <td>321.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>61.384233</td>\n",
       "      <td>61.865183</td>\n",
       "      <td>61.096126</td>\n",
       "      <td>53.472349</td>\n",
       "      <td>70.171760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>29.654395</td>\n",
       "      <td>31.053157</td>\n",
       "      <td>28.554073</td>\n",
       "      <td>33.145982</td>\n",
       "      <td>25.250095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.181800</td>\n",
       "      <td>1.381436</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>0.113173</td>\n",
       "      <td>7.508962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>35.000000</td>\n",
       "      <td>35.354839</td>\n",
       "      <td>35.799999</td>\n",
       "      <td>21.579165</td>\n",
       "      <td>50.930233</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>64.699997</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>64.035004</td>\n",
       "      <td>51.733517</td>\n",
       "      <td>75.196350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>91.099998</td>\n",
       "      <td>91.866524</td>\n",
       "      <td>88.900002</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>92.854828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Total      Female        Male       Rural       Urban\n",
       "count  321.000000  321.000000  321.000000  321.000000  321.000000\n",
       "mean    61.384233   61.865183   61.096126   53.472349   70.171760\n",
       "std     29.654395   31.053157   28.554073   33.145982   25.250095\n",
       "min      2.181800    1.381436    3.600000    0.113173    7.508962\n",
       "25%     35.000000   35.354839   35.799999   21.579165   50.930233\n",
       "50%     64.699997   66.500000   64.035004   51.733517   75.196350\n",
       "75%     91.099998   91.866524   88.900002   86.500000   92.854828\n",
       "max    100.000000  100.000000  100.000000  100.000000  100.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()#描述性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3e912d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "columns = ['Total', 'Female','Male','Rural','Urban']\n",
    "data = df[columns]\n",
    "q1 = np.percentile(data, 25, axis=0)\n",
    "q3 = np.percentile(data, 75, axis=0)\n",
    " \n",
    "# 计算四分位距\n",
    "iqr = q3 - q1\n",
    " \n",
    "# 定义异常值的边界\n",
    "lower_bound = q1 - 1.5 * iqr\n",
    "upper_bound = q3 + 1.5 * iqr\n",
    " \n",
    "# 删除异常值\n",
    "df = df[~((data < lower_bound) | (data > upper_bound)).any(axis=1)]\n",
    " \n",
    "# 绘制箱线图\n",
    "plt.boxplot(data, vert=False, labels=columns)\n",
    "plt.title('Boxplot')\n",
    "plt.xlabel('Value')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "37ce4413",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Countries and areas</th>\n",
       "      <th>Development Regions</th>\n",
       "      <th>Total</th>\n",
       "      <th>Female</th>\n",
       "      <th>Male</th>\n",
       "      <th>Rural</th>\n",
       "      <th>Urban</th>\n",
       "      <th>educational level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Countries and areas, Development Regions, Total, Female, Male, Rural, Urban, educational level]\n",
       "Index: []"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#异常值显示\n",
    "data1= df[((data < lower_bound) | (data > upper_bound)).any(axis=1)]\n",
    "data1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0fb68687",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.to_excel('result_1.xlsx', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "339e43ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Democratic People's Republic of Korea\",\n",
       " 'Ukraine',\n",
       " 'Kazakhstan',\n",
       " 'Turkmenistan',\n",
       " 'Barbados',\n",
       " 'Belarus',\n",
       " 'Bosnia and Herzegovina',\n",
       " 'Kyrgyzstan',\n",
       " 'Montenegro',\n",
       " 'Cuba']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_upper_secondary = df[df['educational level'] == 'Upper secondary']\n",
    "# 计算每个国家的总教育完成率\n",
    "total_completion_rates = df_upper_secondary['Total']\n",
    "# 获取相应的国家列表\n",
    "countries = df_upper_secondary['Countries and areas']\n",
    "countries_list = countries.tolist()\n",
    "total_rates_list = total_completion_rates.tolist()\n",
    "# 对国家按教育完成率进行降序排序\n",
    "sorted_countries = sorted(zip(countries_list, total_rates_list), key=lambda x: x[1], reverse=True)\n",
    "# 获取排名前十的国家\n",
    "top_countries = [country for country, rate in sorted_countries[:10]]\n",
    "top_countries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "98e37b30",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Democratic People's Republic of Korea: 99.9000015258789\n",
      "Ukraine: 96.8000030517578\n",
      "Kazakhstan: 95.1999969482422\n",
      "Turkmenistan: 94.2930450439453\n",
      "Barbados: 93.9000015258789\n",
      "Belarus: 92.3917083740234\n",
      "Bosnia and Herzegovina: 92.1999969482422\n",
      "Kyrgyzstan: 86.8000030517578\n",
      "Montenegro: 86.2450866699219\n",
      "Cuba: 85.6561279296875\n"
     ]
    }
   ],
   "source": [
    "df_upper_secondary = df[df['educational level'] == 'Upper secondary']\n",
    "# 计算每个国家的总教育完成率\n",
    "total_completion_rates = df_upper_secondary['Total']\n",
    "# 获取相应的国家列表\n",
    "countries = df_upper_secondary['Countries and areas']\n",
    "countries_list = countries.tolist()\n",
    "total_rates_list = total_completion_rates.tolist()\n",
    "# 对国家按教育完成率进行降序排序\n",
    "sorted_countries = sorted(zip(countries_list, total_rates_list), key=lambda x: x[1], reverse=True)\n",
    "# 获取排名前十的国家及其教育完成率\n",
    "top_countries_with_rates = sorted_countries[:10]\n",
    "for country, rate in top_countries_with_rates:\n",
    "    print(f\"{country}: {rate}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "06161597",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Female Education Completion Rate: 61.86518343363967\n",
      "Average Male Education Completion Rate: 61.09612562054786\n"
     ]
    }
   ],
   "source": [
    "df['Total'] = pd.to_numeric(df['Total'])\n",
    "df['Female'] = pd.to_numeric(df['Female'])\n",
    "df['Male'] = pd.to_numeric(df['Male'])\n",
    "# 计算男女平均教育完成率\n",
    "avg_female_completion = df['Female'].mean()\n",
    "avg_male_completion = df['Male'].mean()\n",
    "# 打印结果\n",
    "print(f\"Average Female Education Completion Rate: {avg_female_completion}\")\n",
    "print(f\"Average Male Education Completion Rate: {avg_male_completion}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c61283a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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